Centre of Integrative Systems Biology and Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK.
J R Soc Interface. 2012 Oct 7;9(75):2653-66. doi: 10.1098/rsif.2012.0220. Epub 2012 May 2.
We present an analysis of protein interaction network data via the comparison of models of network evolution to the observed data. We take a bayesian approach and perform posterior density estimation using an approximate bayesian computation with sequential Monte Carlo method. Our approach allows us to perform model selection over a selection of potential network growth models. The methodology we apply uses a distance defined in terms of graph spectra which captures the network data more naturally than previously used summary statistics such as the degree distribution. Furthermore, we include the effects of sampling into the analysis, to properly correct for the incompleteness of existing datasets, and have analysed the performance of our method under various degrees of sampling. We consider a number of models focusing not only on the biologically relevant class of duplication models, but also including models of scale-free network growth that have previously been claimed to describe such data. We find a preference for a duplication-divergence with linear preferential attachment model in the majority of the interaction datasets considered. We also illustrate how our method can be used to perform multi-model inference of network parameters to estimate properties of the full network from sampled data.
我们通过将网络演化模型与观测数据进行比较来分析蛋白质相互作用网络数据。我们采用贝叶斯方法，并使用序贯蒙特卡罗近似贝叶斯计算进行后验密度估计。我们的方法允许我们在一系列潜在的网络增长模型中进行模型选择。我们应用的方法使用一种基于图谱定义的距离，它比以前使用的度分布等汇总统计数据更自然地捕获网络数据。此外，我们将采样的影响纳入分析中，以正确纠正现有数据集的不完整性，并在各种采样程度下分析了我们方法的性能。我们考虑了许多模型，不仅关注于生物学上相关的复制模型类，还包括以前被声称描述此类数据的无标度网络增长模型。我们发现，在大多数考虑的相互作用数据集中，复制-分歧与线性优先附着模型的偏好。我们还说明了如何使用我们的方法来进行网络参数的多模型推断，以便从采样数据中估计整个网络的属性。
